Updated_Review2_An Improved Convolutional Neural Network Model for Detection.pptx
- 1. An Improved Convolutional Neural Network Model for Detection of Arecanut Disease and Remedies Under the Guidance of: Project Team
- 2. AGENDA • Abstract • Introduction • Literature Survey • Aim and Objectives • Existing System • Proposed System • System requirement Specifications • References
- 4. ABSTRACT • Arecanut, commonly referred to as betel nut, is a tropical crop. • India holds the second position globally both in its production and consumption. • Throughout its lifecycle, the crop is susceptible to various ailments, spanning from root to fruit. Presently, disease detection primarily relies on visual inspection, requiring farmers to meticulously examine each crop at regular intervals. • In this study, we have introduced a system utilizing Convolutional Neural Networks to identify diseases affecting the leaves, trunk, and overall health of the arecanut plant. • Additionally, the system offers recommended remedies for these identified diseases. • We developed an improved CNN model to distinguish between healthy and diseased arecanut plants. • To facilitate training and testing, we curated a dataset comprising more than 700 images of both healthy and diseased arecanut plants, split into an 80:20 ratio for training and testing, respectively.
- 5. INTRODUCTION • The arecanut palm is the source of common chewing nut, popularly known as betel nut or Supari. In India it is extensively used by large sections of people and is very much linked with religious practices. • India is the largest producer of arecanut and at the same time largest consumer also. Major states cultivating this crop are Karnataka (40%), Kerala (25%), Assam (20%), Tamil Nadu, Meghalaya and West Bengal.
- 6. 'Blast Disease' caused by an air-borne fungus can wipe out 70 percent of arecanut plantations in Karnataka. Published: 24 Nov 2022
- 8. LITERATURE SURVEY SL NO AUTHOR Year Title METHODOLOGY DRAWBACKS 1 Dhanuja K C1 , Mohan Kumar H P August 2020 Areca Nut Disease Detection using Image Processing Technology Applications of Wavelet, Gabor, Local binary (LBP), Gray Level Difference Matrix (GLDM) and Gray Level Co-Occurrence Matrix (GLCM) are used to extract various texture features from areca nut. The automatic configuration of the system is made possible in the future for the classification of areca nuts. 2 Rajashree Krishna,1,* Prema K V2 and Rajat Gaonkar3 2022 Areca Nut Disease Dataset Creation and Validation using Machine Learning Techniques based on Weather Parameters Historical weather data i.e. temperature, rainfall, relative humidity, sunshine, wind direction, and wind speed are collected from the Udupi weather station. Concentrated only on the Dataset creation , No model implementation is done
- 9. SL NO AUTHOR Year Title METHODOLOGY Drawbacks 3 Manpreet Sandhu, Pratik Hadawale 2020 Plant Disease Detection using ML and UAV In this paper the author has developed an automated system for detecting disease using leaf image classification. Cost is more for implementation. 4 Ashish Nage, V.R. Raut 2019 Detection and Identification of Plant Leaf Diseases based on Python the author developed an Android application that helps farmers in identifying plant disease by uploading a leaf image to the system which uses a convolution neural network algorithm to identify the disease in the leaf. person having less knowledge cannot use the mobile application effectively. LITERATURE SURVEY
- 10. SL NO Author Year Title METHODOLOGY Drawbacks 5 Anandhakris hnan MG, Joel Hanson, Annette Joy March 2017 Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network In this paper to get better feature extraction, image reprocessing was done, which includes noise removal, intensity normalization, removing reflections, and masking portions of the image. Feature extraction can be done only for few features . 6 Manisha Bhange, H.A. Hingoliwala 2015 Smart Farming: Pomegranate Disease Detection Using Image Processing In this paper detect pomegranate diseases and also suggest the solution on diseases. Image Augmentation takes longer time than classification 7 Meghana D R and Prabhudeva S June 2022 Image Processing based Arecanut Diseases Detection Using CNN Model In this paper the authors have used different algorithms (SVM, KNN, Decision tree, CNN) for detection of diseases in leaves. If we increase the epochs , Loss will increase to 10%. LITERATURE SURVEY
- 11. PROBLEM STATEMENT The aim of the project is to detect the disease in arecanut fruit, Stem and leaves using improved Convolutional Neural network and Image processing techniques and finding the remedies to the diseases. • Objectives To collect datasets that contain healthy and diseased images of arecanut and their leaves. Design and develop an algorithm for early detection of disease in arecanut that can avoid the spreading of diseases. Develop a deep learning based algorithm that would suggest solutions for the detected diseases. Developing a web application for early detection of disease, prediction and providing remedies to the above solution.
- 12. APPLICATIONS Arecanut disease detection has numerous applications across agriculture, environmental monitoring, and research. Early Disease Detection: Rapid identification of diseases allows for early intervention, preventing widespread damage to crops. This early detection can help in the timely application of treatments or management strategies. Early Disease Detection: Rapid identification of diseases allows for early intervention, preventing widespread damage to crops. This early detection can help in the timely application of treatments or management strategies. Quarantine and Disease Control: Plant disease detection is crucial in quarantine procedures, preventing the spread of diseases to new areas or regions.
- 13. ADVANTAGES Early Detection: Identifying diseases in their early stages allows for prompt action. Preventative Measures: Early detection enables farmers to implement timely using specific pesticides. Reduced Crop Loss: By promptly diagnosing diseases, farmers can take appropriate steps. Optimized Resource Use: Disease detection helps optimize resource utilization minimizing Optimized Resource Use: Disease detection helps optimize resource utilization.
- 14. EXISTING SYSTEM • Visual Inspection: The traditional method involves manual examination by agricultural experts or farmers to identify visual symptoms of diseases, such as leaf discoloration, lesions, spots, or unusual patterns. • Laboratory Techniques: These methods involve taking samples from plants showing symptoms, culturing pathogens in a lab, and using techniques like microscopy, PCR (Polymerase Chain Reaction), ELISA (Enzyme-Linked Immunosorbent Assay), or DNA sequencing to identify specific pathogens causing diseases. • Image Processing: Utilizing cameras and image processing algorithms to capture images of plants and then employing computer vision techniques, machine learning, or deep learning to analyze these images for disease symptoms. This method allows for automated and rapid analysis of large-scale crops. • Machine Learning: Creating machine learning platforms that integrate various data sources (images, environmental factors, and historical data) to diagnose plant diseases.
- 16. SYSTEM REQUIREMENT SPECIFICATIONS • Hardware Requirements Processor: Intel CORE i3 RAM: 4 GB Disk space: minimum 256 GB • Software Requirements Operating System (Windows, MacOS). Python, HTML, CSS, JavaScript. Java / Python Framework and SQL database An Internet Browser (Google Chrome, Microsoft Edge etc). Code Editor (Visual Studio code/PyCharm). The package manager PIP (pip is a python package-management system written in Python used to install and manage software packages).
- 17. Architecture Diagram Input Image Dataset Pre-Processing Training Data Deep Learning Model (Improved CNN) Feature Selection and Segmentation Test Data Model Evaluation Splitting Dataset Recognition Recognition of activity using web Application Healthy Unhealthy
- 18. DATA FLOW DIAGRAM –L0 Deep Learning Model Data Pre-processing Feature Extraction Input ArecaNut Dataset Train Data Test Data Recognition of Arecanut Disease
- 19. SYSTEM DESIGN – Dataflow Diagram-LEVEL1 USER CAPTURE IMAGE Arecanut Disease DETECTION DATASET SYSTEM RECOGNITION
- 20. COLLECT IMAGES of Arecanut Fruit, Stem and Leaves PREPROCESSED &EXTRACT FEATURES DESIGN & CONSTRUCT MODEL TEST THE MODEL IN RAW IMAGE TRAIN MODEL USER ADMIN Use Case Diagram Disease Detection
- 21. REFERENCES 1. Dhanuja K C. Areca Nut Disease Detection using Image Processing Technology. International Journal of Engineering Research 2020 V9. 10.17577/IJERTV9IS080352. 2. Mallaiah, Suresha & Danti, Ajit & Narasimhamurthy, S. Classification of Diseased Arecanut based on Texture Features. International Journal of Computer Applications. 2014. 3. Manpreet Sandhu, Pratik Hadawale, Saumaan Momin, Prof. Ajitkumar Khachane. Plant Disease Detection using ML and UAV. International Research Journal of Engineering and Technology 2020 V7. 4. Mr. Ashish Nage, Prof. V. R. Raut, Detection and Identification of Plant Leaf Diseases based on Python, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT),2019, Volume 08, Issue 05. 5. Anandhakrishnan MG Joel Hanson, Annette Joy, Jeri Francis, Plant Leaf Disease Detection using Deep Learning and Convolutional Neural Network, International Journal of Engineering Science and Computing, Volume 7, Issue No.3, March 2017 6. Manisha Bhange, H.A. Hingoliwala, Smart Farming: Pomegranate Disease Detection Using Image Processing, Procedia Computer Science, Volume 58,2015, Pages 280-288, ISSN 1877-0509.
- 22. REFERENCES 7. Swathy Ann Sam, Siya Elizebeth Varghese, Pooja Murali, Sonu Joseph John, Dr. Anju Pratap. Time saving malady expert system in plant leaf using CNN, 2020, Volume 13, Issue No 3. 8.Detection and classification of areca nuts with machine vision Kuo-Yi Huang 2012 9.Classification of Diseased Areca nut based on Texture Features International Journal of Computer Applications (0975 – 8887) Recent Advances in Information Technology, 2014 10.Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control Limits December 2012. 11.C.L. Hsieh, S.F. Cheng, T.T. Lin, Application of image texture analysis and neural network on the growth stage recognition for head cabbage seedlings, Journal of Agricultural Machinery 6 (2) (1997) 1–13 (in Chinese).
- 23. THANK YOU